Search Results (1,405)

Evaluating Net Primary Productivity (NPP) variations driven by climatic variability and mining activities is fundamental for understanding ecological dynamics in high-intensity open-pit mining areas. Focusing on high-intensity open-pit mining areas of Qian’an, China, from 2016 to 2022, by integrating Sentinel-2, ERA-5 Land reanalysis dataset and Dynamic World V1, we employed an improved Carnegie–Ames–Stanford Approach (CASA) framework alongside the Thornthwaite Memorial algorithm to quantify actual NPP (ANPP) and potential NPP (PNPP). Additionally, the Relative Contribution Index (RCI) was utilized to explicitly isolate mining-driven NPP (MNPP) variations. The results revealed a significant downward trajectory in ANPP within the high-intensity open-pit mining area, with a cumulative reduction of 5.3 × 10⁸ gC a⁻¹. This productivity loss exhibited significant spatial heterogeneity, with the most severe degradation concentrated in core mining districts, including Malanzhuang, Caiyuan, Yangdianzi, and Muchangkou. ANPP, MNPP, and PNPP maintained relative stability overall but displayed significant interannual fluctuations during 2019–2022. RCI analysis indicated MNPP dominated ANPP in 62.67% of the study area, with mining impacts intensifying in 62.83% of the region. Driver mechanisms identified precipitation as the dominant climatic factor enhancing ANPP, whereas mining activities constituted the primary driver of ANPP reduction. Mining accounted for 61.33% of ANPP changes, significantly exceeding climatic variability’s 38.67% contribution. In conclusion, these findings provide a scientific foundation for developing ecological carbon sink systems and optimizing ecological restoration strategies. Full article

Temporal lobe epilepsy (TLE) is the most common focal epilepsy in adults, but cell-type-specific molecular alterations in the epileptic cortex remain incompletely characterized. We performed single-nucleus RNA sequencing on temporal cortex from three patients with drug-resistant TLE and two non-epileptic controls, retaining 66,932 nuclei. Seven major cell types were annotated. Neuropeptide Y (NPY) was significantly upregulated in microglia and oligodendrocytes under stringent criteria (|log₂FC| > 1, adjusted p < 0.01), whereas changes in other cell types did not meet this threshold. Microglia showed enrichment of neuropeptide- and inflammatory-related pathways, together with reduced oxidative phosphorylation signatures. Oligodendrocytes showed altered lipid metabolism, together with reduced mitochondrial energy-related signatures. Inferred intercellular communication was globally reduced in the TLE samples. qPCR in an independent small set showed an upward trend of NPY expression, though not statistically significant. Given the limited cohort size, these results should be interpreted as exploratory. They provide a cell-type-resolved candidate framework for future mechanistic studies of glial-associated responses in human epilepsy. Full article

Accurate classification of rock fracture modes is essential for understanding rock mass instability mechanisms. To address the limitation of traditional acoustic emission (AE) classification methods that treat a single AE signal as a single fracture event, overlooking its composite nature from multiple fracture events and leading to misclassification, this study proposes a novel rock fracture mode classification method based on AE spectral analysis. This study details the development framework, theoretical model, classification criteria, application process, and experimental validation of the new rock fracture mode classification method. Uniaxial compression tests on granite, marble, and limestone, along with rockburst simulation tests on granite, were conducted to validate the classification of fracture modes. In rockburst simulations, shear fracture signals accounted for 48% on average, composite signals 40%, and tensile signals 12%. The method effectively distinguishes multiple fracture events within a single AE signal, accurately classifies fracture modes, and elucidates the dynamic evolution of fracture modes during the rockburst precursor stage, offering significant advantages for rock fracture mode classification and mechanistic insight. Full article

To facilitate the practical deployment and engineering implementation of multi-robot coordination for biomimetic underwater spherical robots (BUSRs), it is imperative to develop a formation tracking control method with a simple structure, a small number of tunable parameters, convenient parameter tuning and strong anti-disturbance capability. This study proposes a formation controller integrating virtual structure (VS), consensus protocol, and parallel output-velocity-type active disturbance rejection control (POV-ADRC), denoted as VS-C-POV-ADRC. A rotating global (RG) coordinate system is established to decouple robot positions from heading angles, which makes the parameter tuning more convenient. A double-loop control architecture is constructed, where the outer consensus control loop generates the desired velocity for each robot based on virtual-structure reference positions, and the inner POV-ADRC loop achieves high-precision velocity tracking. The proposed controller features a compact structure with only five adjustable parameters per motion direction, realizing easy engineering implementation and adaptation to the limited computing capacity of BUSRs. The simulation and experiment results demonstrate that the proposed algorithm enables robots to maintain a stable formation and achieve trajectory tracking accuracy within one body length, while exhibiting superior disturbance rejection. The proposed method provides a feasible and practical solution for BUSR formation control. Full article

Titanium-extracted tailing is a by-product generated during titanium-bearing blast furnace slag treatment process. The crystallization behavior of the titanium-extracted tailing during the cooling process is significant to its utilization for glass ceramics preparation. In this work, the CaO-SiO₂-Al₂O₃-MgO-TiO₂-FeO slag was used to explore the effect of CaO/SiO₂ ratios on titanium-extracted tailing crystallization. FactSage 8.2 calculation and mineralogical characterizations were conducted to investigate the phase and microstructure evolution during the slag cooling process. Single hot thermocouple technique (SHTT) was employed for in situ observation of the crystallization process of the slag during the cooling process. The obtained results indicated that the perovskite, melilite, spinel, diopside and anorthite phases would be crystallized during the cooling process when the CaO/SiO₂ ratios of the slag were 0.7–1.1. Increasing the CaO/SiO₂ ratio to 1.3 and 1.5 promoted the crystallization of olivine and merwinite phases, however, inhibited the crystallization of diopside and anorthite phases. The initial crystallization temperature and the liquid phase disappeared temperature of the slag enhanced with improving CaO/SiO₂ ratios. The initial crystallization temperature was controlled by perovskite phase precipitation when the CaO/SiO₂ ratios of slag reached 0.7–1.3. Whereas the initial crystallization temperature was controlled by the crystallization of spinel phase when the CaO/SiO₂ ratio of slag was 1.5. The incubation time for crystal nucleation reduced with increasing CaO/SiO₂ ratios that promoted slag crystallization. Moreover, increasing the CaO/SiO₂ ratio from 0.7 to 1.5 enhanced the critical cooling rate from 4 °C s⁻¹ to 11 °C s⁻¹. Full article

Under the dual carbon strategy and the sweeping tide of digital transformation in education, higher education confronts an urgent imperative: cultivating talent equipped with interdisciplinary skills and sustainable decision-making capabilities. To meet this critical challenge, this study pioneers the PAMD (Patient Capital–Accounting–Matrix–Development) interdisciplinary teaching framework. Rooted firmly in Education for Sustainable Development (ESD) principles, PAMD uniquely weaves together patient capital, carbon asset accounting, and linear algebra matrix modeling. Utilizing a quasi-experimental design with undergraduate business students, we implemented “Carbon Asset Accounting and Low-Carbon Transition Investment Analysis” as a case study. We rigorously evaluated teaching effectiveness across academic performance, competency, and cognitive attitude dimensions using Welch’s t-test, Hedges’ g, and ANCOVA. After controlling for baseline scores, the experimental group significantly surpassed the control group in comprehensive decision-making (81.22 vs. 72.41, g = 0.71) and matrix modeling competency (3.74 vs. 3.22, g = 0.77). The experimental cohort also demonstrated consistent gains in carbon accounting reporting precision and data representation clarity. Cognitive assessments revealed moderate effect sizes for both low-carbon investment literacy and interdisciplinary learning interest. These compelling results demonstrate that embedding a long-term value orientation into accounting representation and matrix modeling powerfully cultivates students’ ability to transfer interdisciplinary knowledge and make sound sustainable decisions within complex contexts. This study offers a robust, evidence-based, and replicable pathway for driving sustainability-oriented interdisciplinary reform within business education. Full article

Medical images commonly exhibit low contrast, weak boundaries, and complex textures. In addition, significant semantic differences exist between deep-level semantic features and shallow-level detail features, posing challenges for multi-scale feature fusion in terms of detail preservation and structural consistency. To address these issues, a frequency-enhanced and bidirectional feature-guided segmentation network (FBNet) is proposed. The network comprises two core components. The frequency-based enhancement (FBE) module employs the Fast Fourier Transform and applies adaptive modulation to the amplitude spectrum through a content-aware gating mechanism, enhancing detail expression and inter-structural contrast. The Bidirectional Guided Feature Fusion module (BGF) enables bidirectional interaction between shallow and deep features. Additionally, the Structure and Edge Awareness (SEA) module is constructed using directional and variance attention mechanisms to achieve collaborative optimization of structural modeling and edge perception. Experiments on four medical image segmentation datasets show that, compared to the second-best method, FBNet achieves improvements of 2.12, 1.57, 1.37, and 1.56 percentage points on the mIoU metric and 1.54, 1.11, 0.84, and 1.03 percentage points on the mDice metric. Full article

Remote ischemic conditioning (RIC) is an endogenous strategy that mitigates cerebral injury in preclinical stroke models. However, its bench-to-bedside translation is frequently hindered by complex patient environments that induce RIC resistance and limit its neuroprotective efficacy. To bridge this translational gap, this review systematically examines the extrinsic pathophysiological and pharmacological barriers to RIC. We categorize RIC resistance into three mechanism-driven phenotypes. Impaired signal initiation (Type I) is often linked to diabetic sensorimotor polyneuropathy and the reactive oxygen species-scavenging effects of propofol. Signal transmission blockade (Type II) is associated with specific P2Y12 inhibitors and smoking-induced endothelial dysfunction. Furthermore, effector desensitization (Type III) involves target-organ unresponsiveness exacerbated by aging, chronic hyperglycemia, and postmenopausal estrogen depletion. To address these barriers, potential phenotype-specific optimization strategies are discussed. Ultimately, transitioning from generalized empirical protocols to mechanism-based precision strategies may help bypass RIC resistance in clinical settings and enhance stroke cerebroprotection. Full article

Coal gangue (CG), a bulk solid waste produced during coal mining, is rich in active components such as silicon and aluminum oxides, making it a high-quality raw material for the production of cementitious materials. Its utilization represents a significant pathway for achieving high-value applications of CG and facilitating the low-carbon transformation of the cement industry. Owing to advantages such as low carbon emissions, environmental friendliness, cost-effectiveness, and tunable performance, CG-based cementitious materials have been extensively investigated by researchers worldwide. Studies have focused on various aspects, including cementitious backfill materials, CG solid waste-based cement, geopolymers, concrete, and composite materials derived from CG. This paper systematically reviews the regional distribution, mineral composition, chemical constituents, and reactivity characteristics of CG. It further summarizes recent advances in activation techniques, performance optimization, and engineering applications of CG-based cementitious materials. Current challenges, such as insufficient activation efficiency, ambiguous hydration mechanisms, and limitations in large-scale application, are critically analyzed. Finally, future research directions and development trends are outlined to provide a theoretical foundation for further investigation and industrial implementation of CG-based cementitious materials. Full article

In intelligent excavator applications, traditional excavator posture recognition methods face two major challenges: limited recognition accuracy and insufficient computing resources on edge devices. To address these issues, this study proposes an excavator posture recognition method based on an improved Real-Time Detection Transformer (RT-DETR). First, a new backbone network is designed based on the Reparameterized Vision Transformer to improve feature utilization efficiency while reducing computational demands. Next, the overall architecture is optimized by introducing lightweight Dynamic Upsamplers, which reduce information loss during upsampling and enhance multi-scale feature fusion. In addition, a Cross-Attention Fusion Module is adopted to strengthen local feature extraction while retaining the global modeling capability of the Transformer, thereby improving the discrimination between foreground and background. Finally, a Multi-Scale Fusion Network is introduced to further enhance the multi-scale feature representation ability of RT-DETR. Experimental results show that the proposed method achieves a mean average precision (mAP) of 94.29% for small object detection, which is 7.96% higher than that of the baseline RT-DETR, while reducing the number of model parameters by 34.95%. Compared with YOLO-series models, the proposed method improves mAP by 8.62% to 12.75%. These results indicate that the proposed method outperforms existing methods in both detection accuracy and computational efficiency and provides an efficient and feasible solution for real-time excavator posture recognition. Full article

Droplet microfluidics has been widely used in biological, chemical, and medical research owing to its advantages of miniaturization, high throughput, and low reagent consumption. However, limited sensitivity and optical path length in on-chip absorbance detection remain major challenges for droplet-based microfluidic analysis. Traditional absorbance detection suffers from low sensitivity due to the extremely short optical path in microfluidic channels, while existing optical path extension methods have drawbacks such as complex fabrication, easy droplet rupture, or strict incident angle requirements. To address these issues, this study developed a droplet microfluidic absorbance detection platform integrating optical fibers, on-chip micromirrors, external fluidic actuation, and an absorbance detection module. Microchannel sidewalls filled with low-melting-point metal act as mirrors; the multi-reflection optical path, combined with optical fibers and micromirrors, compensates for insufficient light manipulation and effectively extends the absorption path length, improving sensitivity and accuracy. Using this method, the detection limit for methylene blue solution was 20 μM, and the sensitivity for Escherichia coli (E. coli) suspension was doubled compared with traditional Nanodrop OD600 measurement. This device features low fabrication difficulty and cost and stable detection, providing a proof-of-concept strategy for enhanced absorbance detection in droplet microfluidic systems. Full article

In the North China Plain (NCP), wind and rain during the grain-filling period of winter wheat can cause lodging. The second basal internode (I2), a key load-bearing structure, plays a central role in yield stability. This study, under a constant nitrogen (N) application rate of 270 kg ha⁻¹, aimed to clarify how nitrogen basal-to-topdressing ratios regulate I2 characteristics to balance lodging resistance and yield increase. Field experiments were conducted across two seasons with three cultivars and three nitrogen split ratios (5:5, CK; 3:7, N1; and 7:3, N2). Dynamic measurements of I2 mechanical properties, morphology, anatomy, and composition were taken, and structural equation modeling (SEM) was used for analysis. Results showed that the culm lodging resistance index (CLRI) decreased by 41.8% from flowering to milk stage under all treatments, with CLRI at the milk stage of lodging treatments between 0.11 and 0.15. SEM supported a composition–structure–lodging resistance–yield chain, with CLRI as the key mediator. The N1 treatment significantly improved CLRI at all stages and increased yield by 12.2% compared to CK, making it a recommended nitrogen strategy for improving both yield and lodging resistance. These findings provide agronomically applicable nitrogen management guidelines for high-yield winter wheat systems. Full article

Magnetic fluid sealing is an ideal solution for high-end equipment. However, traditional rectangular pole teeth suffer from low magnetic flux utilization and insufficient pressure resistance. Meanwhile, the pressure transmission mechanism of different pole teeth and the evolution law of magnetic fluid boundary morphology remain unclear, restricting structural optimization. This study investigates rectangular and trapezoidal pole teeth by adopting the Volume of Fluid model, combined with finite element simulation and experimental verification. A sealing simulation model and a dedicated experimental platform were established to systematically explore the effects of the two pole tooth types on pressure transmission efficiency and magnetic fluid boundary morphology under static and dynamic sealing conditions, as well as their pressure resistance and self-recovery characteristics. Results show that trapezoidal pole teeth exhibit superior pressure resistance to rectangular ones due to optimized magnetic field distribution: the maximum static sealing pressure resistance increases by 40.9 kPa, and the dynamic sealing pressure resistance at 8000 rpm rises by 63.2 kPa. The 2% deviation between simulation and experimental data verifies the model’s reliability. This work clarifies the intrinsic relationship between pole tooth structure and sealing performance, reveals the pressure transmission mechanism of different pole teeth, and provides theoretical and engineering references for pole tooth structural optimization, which is significant for improving the pressure resistance stability and engineering applicability of magnetic fluid sealing. Full article

Lactic acid bacteria (LAB) are increasingly used as functional feed additives in aquaculture. This study evaluated the effects of dietary Lactobacillus plantarum supplementation on growth performance, hepatic biochemical status, lipid-related indices, antioxidant status, and immune-related responses in Chinese tongue sole (Cynoglossus semilaevis). A total of 1620 juveniles (initial body weight 8.11 ± 0.23 g) were randomly assigned to three dietary treatments for 10 weeks: a control diet (CON) and diets supplemented with 500 mg/kg (LAB1) or 1000 mg/kg (LAB2) LAB (three replicate tanks per treatment). Compared with CON, LAB supplementation improved growth performance, with LAB2 showing significantly higher final body weight, weight gain rate, and specific growth rate. LAB altered hepatic function-related indices and reduced hepatic lipid peroxidation, as indicated by lower malondialdehyde (MDA). Hepatic lipid-related indices were also modulated, with LAB2 showing reduced total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triacylglycerol (TG), and total bile acid (TBA), together with increased high-density lipoprotein cholesterol (HDL-C). Serum immune indices showed non-linear responses, with C3, C4, and IgM increasing in LAB1 but decreasing in LAB2, whereas lysozyme showed an overall decreasing trend. qPCR analysis showed that LAB supplementation upregulated hepatic IGF-I and TGF-β1 expression, downregulated IL-8, TNF-α, and G6PD expression, and reduced FAS expression at the higher dose. Overall, dietary L. plantarum at 500–1000 mg/kg improved growth performance and was associated with changes in hepatic lipid-related indices, oxidative status, and immune-related responses in tongue sole. These results support further evaluation of L. plantarum as a functional feed additive in this species. Full article

Wafer defect analysis is important for semiconductor manufacturing, but labeled data are limited, and class distributions are highly imbalanced. We present a semi-supervised framework with two lightweight hybrid CNN–Transformer models for wafer defect classification and segmentation. For classification, HybridCNN-ViT combines CNN-based local feature extraction with Transformer-based global context modeling, and adopts a three-stage progressive pseudo-labeling strategy to leverage unlabeled samples. The pseudo-label selection mechanism is systematically calibrated to improve pseudo-label reliability under limited labeled data. For segmentation, ConvoFormer-UNet integrates convolution-enhanced embeddings with Transformer blocks to balance boundary detail and global context. On the public WM-811K dataset, HybridCNN-ViT achieves 98.72% accuracy and 0.9985 macro-AUC under the semi-supervised setting for classification, while ConvoFormer-UNet reaches 99.19% IoU for segmentation with fewer parameters than several baselines. We also report efficiency on a single GPU to illustrate practical inference speed. Full article